Short Sales, Long Sales, and the Lee-Ready Trade Classification Algorithm Revisited

Asquith, Oman, and Safaya (2010) conclude that short sales are often misclassified by the Lee-Ready algorithm. The algorithm identifies most short sales as buyer-initiated, whereas the authors posit that short sales should be overwhelmingly seller-initiated. Using order data to identify true trade initiator, we document that short sales are, in fact, predominantly buyer-initiated and that the Lee-Ready algorithm correctly classifies most of them. Misclassification rates for short and long sales are near zero at the daily level. At the trade level, misclassification rates are 31% using contemporaneous quotes and trades and decline to 21% when quotes are lagged one second

Algorithmic Trading and Information

We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse. AT liquidity demand represents 52% of volume and AT supplies liquidity on 50% of volume. AT act strategically by monitoring the market for liquidity and deviations of price from fundamental value. AT consume liquidity when it is cheap and supply liquidity when it is expensive. AT contribute more to the efficient price by placing more efficient quotes and AT demanding liquidity to move the prices towards the efficient price

Impersonal efficiency and the dangers of a fully automated securities exchange

This report identifies impersonal efficiency as a driver of market automation during the past four decades, and speculates about the future problems it might pose. The ideology of impersonal efficiency is rooted in a mistrust of financial intermediaries such as floor brokers and specialists. Impersonal efficiency has guided the development of market automation towards transparency and impersonality, at the expense of human trading floors. The result has been an erosion of the informal norms and human judgment that characterize less anonymous markets. We call impersonal efficiency an ideology because we do not think that impersonal markets are always superior to markets built on social ties. This report traces the historical origins of this ideology, considers the problems it has already created in the recent Flash Crash of 2010, and asks what potential risks it might pose in the future

THREE ESSAYS ON NYSE SPECIALIST STRATEGIES

Thesis (PhD) - Indiana University, Economics, 2005In our first essay, we investigate how the New York Stock Exchange (NYSE) specialists react to the changes in market variables while making participation decisions to the posted quotes by analyzing specialists' choices to undercut or add depth to the limit order book. We find that the primary factor that affects the participation strategy of the specialists in the current period is the changes in the best prices and depths on the limit order book. In addition, specialists participate to the posted quotes more for volatile or low volume stocks. The levels of specialists' participation in the posted quotes have predictive power over future stock returns. This predictive power is stronger for short-term returns. In our second essay, we analyze trading strategies of the specialists conditional on their decisions to participate in the current posted quotes. We find that the specialists use limit order book asymmetry and cumulative order imbalance as two information sources about the true security value. If the relative size of the market order is high, specialists choose not to participate and let the market order trade with the limit order book. Consistent with the theoretical results in the previous literature, specialists trade more aggressively when the spread is large. We also find significant inventory effects. The specialists trade more aggressively, if the trade with the incoming market order restores their inventories. Our third essay shows that there exist significant differences between the performances of "individual" specialists in terms of quotes, depths, spreads and execution costs. We find that, as the trading frequency increases, order processing costs increase for both the specialist firms and individual specialist portfolios, which is consistent with the hypothesis that profits from active stocks subsidize inactive stocks. We also show that individual NYSE specialists differ significantly in their participation strategies to the posted quotes and trades. This suggests that there are significant differences in the execution costs between specialists, because they use different strategies

Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders

We consider issues of time in automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. In particular, we explore two effects: (i) reaction speed - the time taken for trading strategies to calculate a response to market events; and (ii) trading urgency - the sensitivity of trading strategies to approaching deadlines. Much of the literature on trading agents focuses on optimising pricing strategies only and ignores the effects of time, while real-world markets continue to experience a race to zero latency, as automated trading systems compete to quickly access information and act in the market ahead of others. We demonstrate that modelling reaction speed can significantly alter previously published results, with simple strategies such as SHVR outperforming more complex adaptive algorithms such as AA. We also show that adding a pace parameter to ZIP traders (ZIP-Pace, or ZIPP) can create a sense of urgency that significantly improves profitability.Comment: 22 pages. To be published in A. P. Rocha et al. (Eds.), ICAART 2020, LNAI 12613, 2021. arXiv admin note: substantial text overlap with arXiv:1912.0277

Stochastic Models of Limit Order Markets

During the last two decades most stock and derivatives exchanges in the world transitioned to electronic trading in limit order books, creating a need for a new set of quantitative models to describe these order-driven markets. This dissertation offers a collection of models that provide insight into the structure of modern financial markets, and can help to optimize trading decisions in practical applications. In the first part of the thesis we study the dynamics of prices, order flows and liquidity in limit order markets over short timescales. We propose a stylized order book model that predicts a particularly simple linear relation between price changes and order flow imbalance, defined as a difference between net changes in supply and demand. The slope in this linear relation, called a price impact coefficient, is inversely proportional in our model to market depth - a measure of liquidity. Our empirical results confirm both of these predictions. The linear relation between order flow imbalance and price changes holds for time intervals between 50 milliseconds and 5 minutes. The inverse relation between the price impact coefficient and market depth holds on longer timescales. These findings shed a new light on intraday variations in market volatility. According to our model volatility fluctuates due to changes in market depth or in order flow variance. Previous studies also found a positive correlation between volatility and trading volume, but in order-driven markets prices are determined by the limit order book activity, so the association between trading volume and volatility is unclear. We show how a spurious correlation between these variables can indeed emerge in our linear model due to time aggregation of high-frequency data. Finally, we observe short-term positive autocorrelation in order flow imbalance and discuss an application of this variable as a measure of adverse selection in limit order executions. Our results suggest that monitoring recent order flow can improve the quality of order executions in practice. In the second part of the thesis we study the problem of optimal order placement in a fragmented limit order market. To execute a trade, market participants can submit limit orders or market orders across various exchanges where a stock is traded. In practice these decisions are influenced by sizes of order queues and by statistical properties of order flows in each limit order book, and also by rebates that exchanges pay for limit order submissions. We present a realistic model of limit order executions and formalize the search for an optimal order placement policy as a convex optimization problem. Based on this formulation we study how various factors determine investor's order placement decisions. In a case when a single exchange is used for order execution, we derive an explicit formula for the optimal limit and market order quantities. Our solution shows that the optimal split between market and limit orders largely depends on one's tolerance to execution risk. Market orders help to alleviate this risk because they execute with certainty. Correspondingly, we find that an optimal order allocation shifts to these more expensive orders when the execution risk is of primary concern, for example when the intended trade quantity is large or when it is costly to catch up on the quantity after limit order execution fails. We also characterize the optimal solution in the general case of simultaneous order placement on multiple exchanges, and show that it sets execution shortfall probabilities to specific threshold values computed with model parameters. Finally, we propose a non-parametric stochastic algorithm that computes an optimal solution by resampling historical data and does not require specifying order flow distributions. A numerical implementation of this algorithm is used to study the sensitivity of an optimal solution to changes in model parameters. Our numerical results show that order placement optimization can bring a substantial reduction in trading costs, especially for small orders and in cases when order flows are relatively uncorrelated across trading venues. The order placement optimization framework developed in this thesis can also be used to quantify the costs and benefits of financial market fragmentation from the point of view of an individual investor. For instance, we find that a positive correlation between order flows, which is empirically observed in a fragmented U.S. equity market, increases the costs of trading. As the correlation increases it may become more expensive to trade in a fragmented market than it is in a consolidated market. In the third part of the thesis we analyze the dynamics of limit order queues at the best bid or ask of an exchange. These queues consist of orders submitted by a variety of market participants, yet existing order book models commonly assume that all orders have similar dynamics. In practice, some orders are submitted by trade execution algorithms in an attempt to buy or sell a certain quantity of assets under time constraints, and these orders are canceled if their realized waiting time exceeds a patience threshold. In contrast, high-frequency traders submit and cancel orders depending on the order book state and their orders are not driven by patience. The interaction between these two order types within a single FIFO queue leads bursts of order cancelations for small queues and anomalously long waiting times in large queues. We analyze a fluid model that describes the evolution of large order queues in liquid markets, taking into account the heterogeneity between order submission and cancelation strategies of different traders. Our results show that after a finite initial time interval, the queue reaches a specific structure where all orders from high-frequency traders stay in the queue until execution but most orders from execution algorithms exceed their patience thresholds and are canceled. This "order crowding" effect has been previously noted by participants in highly liquid stock and futures markets and was attributed to a large participation of high-frequency traders. In our model, their presence creates an additional workload, which increases queue waiting times for new orders. Our analysis of the fluid model leads to waiting time estimates that take into account the distribution of order types in a queue. These estimates are tested against a large dataset of realized limit order waiting times collected by a U.S. equity brokerage firm. The queue composition at a moment of order submission noticeably affects its waiting time and we find that assuming a single order type for all orders in the queue leads to unrealistic results. Estimates that assume instead a mix of heterogeneous orders in the queue are closer to empirical data. Our model for a limit order queue with heterogeneous order types also appears to be interesting from a methodological point of view. It introduces a new type of behavior in a queueing system where one class of jobs has state-dependent dynamics, while others are driven by patience. Although this model is motivated by the analysis of limit order books, it may find applications in studying other service systems with state-dependent abandonments

Three essays on commodity markets

This dissertation consists of three essays that investigate issues in agricultural commodity futures and cash markets. The first essay uses price discovery measures and intraday data to quantify the proportional contribution of nearby and deferred contracts in price discovery in the corn and live cattle futures markets. On average, nearby contracts reflect information more quickly than deferred contracts in the corn market but have a relatively less dominant role in the live cattle market. In both markets, the nearby contract loses dominance when its relative volume share dips below 50%, which typically occurs when the nearby is close to maturity. Regression results indicate that the share of price discovery is mainly related to trading volume and time to expiration in both markets. In the corn market, the price discovery share between nearby and deferred contracts is also related to inverse carrying charges, crop year differences, USDA announcements, market crashes, and commodity index position rolls. Differences between corn and live cattle markets are consistent with differences in the contracts’ liquidity and commodity storability. The second essay investigates the effect of algorithmic trading activity, as measured by quoting, on the corn, soybean, and live cattle commodity futures market quality. Using the CME’s limit-order-book data and a heteroskedasticity-based identification approach, we find more intensive algorithmic quoting (AQ) is beneficial in multiple dimensions of market quality. On average, AQ improves pricing efficiency and mitigates short-term volatility, but its effects on liquidity costs are somewhat mixed. Increased AQ significantly narrows effective spreads in the corn and soybean markets, but not in the less traded live cattle futures market. The narrowing in effective spreads emerges from a reduction in adverse selection costs as more informed traders lose their market advantage. There also is evidence that liquidity provider revenues increase with heightened AQ activity in the corn futures market, albeit the effect is not statistically significant in the soybean and live cattle futures markets. The third essay investigates how export prices and sales responses to exchange rate movements are affected by the level of the stocks-to-use ratio. The analysis is performed in the corn, soybean, and wheat export markets using Threshold Vector Autoregressive (TVAR) models and monthly data for the January 1990-December 2019 period. Both importer and exporter exchange rates are considered in our analysis. Results show that the effects of both importer and exporter exchange rates on corn export prices and sales are either insignificant or have small economic value due to the relatively small export share of production. In the more export-oriented soybean and wheat markets, an increase in the value of the dollar relative to other exporters’ currencies causes an expected and significant decrease in the export price, but export sales are not significantly affected which reflects the low substitutability between the U.S. exports and competitors’ exports in terms of marketing seasons and crop classes. The effects of importer exchange rates present significant threshold effects in soybean and wheat markets as export prices and sales are more responsive in the low regime of stocks-to-use ratio. Similar threshold effects are also found in the exporter exchange rate impacts on corn export prices and sales. However, the impacts across regimes are not largely different in economic value

Three Essays on Informed Trading

This thesis consists of three essays examining the behavior of informed traders in financial markets and how they affect asset pricing. It examines informed traders’ role in shaping securities prices in three ways. It examines whether on a macro and micro basis insider traders move prices to a different degree than non-insiders. In addition, it uses econometric methods to determine what exchange generates permanent price trends in UK shares. Lastly, it looks at another side effect of fragmentation – how a ‘best execution’ mandate and related market structure changes affect transactions costs in liquid UK, French, and German shares. These studies expand on current literature in various ways – extant insider trading literature has either primarily focused on daily price movement and volume or had consisted of case studies, the conclusions of which may be idiosyncratic and therefore unrepresentative of typical insider behavior. The new phenomenon of multilateral trading facilities (also known as electronic communications networks) and the proliferation of algorithmic or computer-mediated trading had not been examined in price discovery papers, due to their relative novelty. In addition, despite a bevy of literature offering informed insight into the impact of the European Union’s Markets in Financial Instruments Directive (MiFID), there has been a dearth of empirical studies assessing its impact on European securities markets. Chapters 2 and 3 examine MiFID and computerized trading from two different perspectives: that of which trades lead to permanent prices, and that of transactions costs. The conclusions drawn in this thesis will be of interest to regulators, market operators, and traders, as they offer insight into the impact of market structure and how it impacts informed traders who participate in them